化工学报2019，Vol.70Issue(12)：4741-4748,8.DOI:10.11949/0438-1157.20190606
基于k-近邻互信息的发酵过程高斯过程回归建模
Gaussian process regression modeling of fermentation process based on k-nearest neighbor mutual information
摘要
Abstract
The concentration of the substrate during fermentation is often not measured online. In this paper, Gaussian process regression (GPR) is used to establish an estimation model of substrate concentration, and its soft measurement is realized. Different from traditional regression models, the GPR model can not only predict the quality value, but also provide the estimation variance. In order to improve the prediction performance of the model in the nonlinear fermentation process with correlated variables, the input variables of the model are selected by the k-nearest neighbor mutual information (k-MI) method before the model development. The application results of penicillin fermentation process show the ideal prediction performance based on the kMI-GPR model.关键词
发酵过程/高斯过程回归/k-近邻互信息/软测量Key words
fermentation process/ Gaussian process regression/ k-nearest neighbor mutual information/ soft sensor分类
信息技术与安全科学引用本文复制引用
赵荣荣,赵忠盖,刘飞..基于k-近邻互信息的发酵过程高斯过程回归建模[J].化工学报,2019,70(12):4741-4748,8.基金项目
国家自然科学基金项目(61573169) （61573169）
